code: def test_shard_episodes_iterator(self):
      class DummyEpisode:
          def __init__(self, length):
              self.length = length
              # Dummy data to represent the episode content.
              self.data = [0] * length
  
          def __len__(self):
              return self.length
  
          def __getitem__(self, key):
              assert isinstance(key, slice)
              # Create a new Episode object with the sliced length
              return DummyEpisode(len(self.data[key]))
  
          def __repr__(self):
              return f"{(type(self).__name__)}({self.length})"
  
      # Create a list of episodes with varying lengths
      episode_lens = [10, 21, 3, 4, 35, 41, 5, 15, 44]
  
      episodes = [DummyEpisode(len_) for len_ in episode_lens]
  
      # Number of shards
      num_shards = 3
      # Create the iterator
      iterator = ShardEpisodesIterator(episodes, num_shards)
      # Iterate and collect the results
      shards = list(iterator)
      # The sharder should try to split as few times as possible. In our
      # case here, only the len=4 episode is split into 1 and 3. All other
      # episodes are kept as-is. Yet, the resulting sub-lists have all
      # either size 59 or 60.
      check([len(e) for e in shards[0]], [44, 10, 5])  # 59
      check([len(e) for e in shards[1]], [41, 15, 3])  # 59
      check([len(e) for e in shards[2]], [35, 21, 1, 3])  # 60
  
      # Different number of shards.
      num_shards = 4
      # Create the iterator.
      iterator = ShardEpisodesIterator(episodes, num_shards)
      # Iterate and collect the results
      shards = list(iterator)
      # The sharder should try to split as few times as possible, keeping
      # as many episodes as-is (w/o splitting).
      check([len(e) for e in shards[0]], [44])  # 44
      check([len(e) for e in shards[1]], [41, 3])  # 44
      check([len(e) for e in shards[2]], [35, 10])  # 45
      check([len(e) for e in shards[3]], [21, 15, 5, 1, 3])
  
file_path: ray/python/ray/rllib/utils/tests/test_minibatch_utils.py
imports: import unittest
  import numpy
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.minibatch_utils import MiniBatchCyclicIterator, ShardEpisodesIterator
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 2 for calling the TestMinibatchUtils obj's underlying member method code with example usage: obj.test_shard_episodes_iterator() and returns: self, DummyEpisode, type, str, self, str